The general populace interacts with a wide variety of sensors on a daily basis and vast amounts of data pertaining to individuals and entire groups of people is collected from these sensors. This data can be anchored in the physical realm, such as location data provided through a GPS sensor, caloric expenditure provided by an exercise machine, footstep count provided by an accelerometer-based step counter, or heart rate, body temperature, respiratory rate, or glucose level provided by a biometric sensor. This data can alternatively be anchored in the digital realm, such as interests as indicated by websites visited or needs as indicated by purchases made through an online store. Such data can provide significant insight into market trends and needs, interests, and expectations of a particular user or demographic. Furthermore, this data can even be used to target a user with particular experience, physical good or service, or digital good or service. However, contemporary sensors, data collection, and data analysis fail to capture cognitive, mental, and affective states of individuals and groups of people that can provide similar insight and improve user experiences and abilities. Furthermore, contemporary data collection fails to efficiently locate, obtain, and aggregate biosignal data from multiple or selected individuals and make this data available for analysis. Thus, there is a need in the biosignals field for a new and useful system and method for instructing a behavior change in a user.